The role of race and ethnicity in leisure participation among children and youth with disabilities: a systematic review
Bibliographic record
Abstract
PURPOSE: Racial and ethnic minoritized children and youth with disabilities participate less often in structured leisure activities compared to their white peers and yet, little is known about the role of race/ethnicity in their participation. The purpose of this review was to understand the role of race/ethnicity in leisure participation of children and youth with disabilities. METHODS: We systematically assessed peer-reviewed studies published from 2000 to 2023 in six international databases. We independently screened and identified thirteen studies meeting our inclusion criteria. RESULTS: Three themes emerged from our findings: (1) rates of leisure participation; (2) factors affecting leisure participation (i.e., ableism and racism; fear of harassment and safety; systemic factors; disability-related factors and intersectional factors); and (3) benefits and impact of culture on leisure participation (i.e., perceived benefits of leisure participation; perceived constraints of leisure participation; parents' advocacy; importance of family participation). CONCLUSIONS: Findings revealed how disability and racial discrimination, systemic factors, cultural preferences, and availability of financial resources all shape leisure experiences and rate of participation for racial/ethnic minoritized children and youth with disabilities. Future research should explore the impact of culture on leisure participation in more depth.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".